Multimodal Distillation and Fusion for Enhanced Age-Related Macular Degeneration Classification

Age-related macular degeneration (AMD) is a leading cause of vision impairment and blindness in the elderly, making accurate diagnosis and fine-grained sub type classification essential for clinical decision-making. However, existing methods often fail to fully leverage complementary information from multiple imaging modalities due to complex lesion characteristics and heterogeneous data distributions. To address these challenges, we pro pose a novel Multimodality Distillation and Fusion Network (ModiF) for robust AMD classification. First, modality specific feature extractors are designed to capture hierarchical semantic representations from different imaging modalities such as OCT, OCTA, and CFP. To further enhance feature discriminability, an auxiliary cross-modal soft supervision strategy is introduced during training, in which modality-specific predictions are adaptively voted and used as soft targets for knowledge distillation. Building on these modality-specific features, a principal-modality guided distillation mechanism is designed to facilitate effective cross-modality knowledge transfer while preserving critical diagnostic information. Furthermore, a confidence aware fusion module is introduced to adaptively balance the contributions of different modalities by dynamically adjusting fusion weights based on the entropy of modality specific predictions. Experimental validations on one internal dataset (OCT and OCTA) and two public datasets (CFP andOCT)demonstrate that ModiF consistently outperforms existing state-of-the-art methods across different settings, highlighting its effectiveness and generalization capability in AMD classification.